Expectation Propagation for Rectified Linear Poisson Regression

The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads to measurement noise that behaves very differently from additive white Gaussian noise. To address the intractable inference problem for such models, we present a novel efficient and robust Expectation Propagation algorithm entirely based on analytically tractable computations operating re- liably in regimes where quadrature based implementations can fail. Full posterior inference therefore becomes an attractive alternative in areas generally dominated by methods of point estimation. Moreover, we discuss the rectified linear function in the context of other common non-linearities and identify situations where it can serve as a robust alternative.

Published in:
Proceedings of the Seventh Asian Conference on Machine Learning
Presented at:
7th Asian Conference on Machine Learning (ACML), Hong Kong, November 20-22, 2015

 Record created 2015-12-04, last modified 2018-09-13

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